A Hybrid EfficientNetV2S–ResNet50 Model for the Accurate Classification of Plant Leaf Diseases
Received: 15 December 2025 | Revised: 5 January 2026, 25 January 2026, 15 February 2026, 20 February 2026, and 26 February 2026 | Accepted: 27 February 2026 | Online: 4 April 2026
Corresponding author: Fatima-Zahra Fandi
Abstract
Accurate classification of plant leaf diseases is significant for precision agriculture. This study details the development and evaluation of a hybrid Deep Learning (DL) architecture integrating EfficientNetV2S and ResNet50 to balance accuracy and computational efficiency. The original 8,000-image PlantVillage dataset was augmented to 28,000 images to address class imbalance and enable robust training. The dataset was partitioned into 20,000 images for training, 4,000 for validation, and 4,000 for testing. The hybrid model successfully leveraged the complementary strengths of both components, achieving a final classification accuracy of 97.16%, thereby demonstrating its suitability for real-time agricultural monitoring. This work confirms the potential of feature-fusion strategies for practical, early-stage disease screening (healthy versus diseased).
Keywords:
plant disease classification, deep learning, EfficientNetV2S, ResNet50, hybrid model, precision agricultureDownloads
References
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90, Feb. 2017. DOI: https://doi.org/10.1145/3065386
F. F. Zahra, G. Mohamed, and A. Mohamed, "Plant Health—Detecting Leaf Diseases: A Systematic Review of the Literature," in Engineering Applications of Artificial Intelligence, A. Chakir, J. F. Andry, A. Ullah, R. Bansal, and M. Ghazouani, Eds. Springer Nature Switzerland, 2024, pp. 219–235. DOI: https://doi.org/10.1007/978-3-031-50300-9_12
S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, vol. 7, Sept. 2016.
K. P. Ferentinos, "Deep learning models for plant disease detection and diagnosis," Computers and Electronics in Agriculture, vol. 145, pp. 311–318, Feb. 2018. DOI: https://doi.org/10.1016/j.compag.2018.01.009
M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks." arXiv, Sept. 11, 2020.
M. Tan and Q. Le, "EfficientNetV2: Smaller Models and Faster Training," in Proceedings of the 38th International Conference on Machine Learning, July 2021, pp. 10096–10106.
K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016, pp. 770–778. DOI: https://doi.org/10.1109/CVPR.2016.90
Z.-H. Zhou, Ensemble Methods: Foundations and Algorithms. New York, NY, USA: Chapman and Hall/CRC, 2012. DOI: https://doi.org/10.1201/b12207
S. P. Mohanty, "PlantVillage-Dataset", Available: https://github.com/spMohanty/PlantVillage-Dataset/tree/master/raw/color.
S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, vol. 7, Sep. 2016. DOI: https://doi.org/10.3389/fpls.2016.01419
M. Sokolova and G. Lapalme, "A systematic analysis of performance measures for classification tasks," Information Processing & Management, vol. 45, no. 4, pp. 427–437, July 2009. DOI: https://doi.org/10.1016/j.ipm.2009.03.002
E. C. Too, L. Yujian, S. Njuki, and L. Yingchun, "A comparative study of fine-tuning deep learning models for plant disease identification," Computers and Electronics in Agriculture, vol. 161, pp. 272–279, June 2019. DOI: https://doi.org/10.1016/j.compag.2018.03.032
Downloads
How to Cite
License
Copyright (c) 2026 Fatima-Zahra Fandi, Mohamed Ghazouani, Mohamed Azzouazi
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
